Effects of Measurement Errors on Individual Tree Stem Volume Estimates for the Austrian National Forest Inventory

Berger, Ambros; Gschwantner, Thomas; McRoberts, Ronald E.; Schadauer, Klemens

doi:10.5849/forsci.12-164

Cited by 66 publications

(74 citation statements)

References 29 publications

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“…All NFI data were considered to be ground truth. Especially for small-scale predictions, as in this study, however, these errors could increase the variances of the predictions and introduce a bias [30].…”

Section: Discussionmentioning

confidence: 97%

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Rahlf

Breidenbach

Solberg

et al. 2015

Forests

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Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS). Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA) and the individual tree crown (ITC) approach. In this article, we apply the semi-ITC approach, a variety of the ITC approach, on an image-based point cloud to predict forest parameters and compare the performance to the ABA. Norwegian National Forest Inventory sample plots on a site in southeastern Norway were used as the reference data. Tree crown objects were delineated using a watershed segmentation algorithm, and explanatory variables were calculated for each tree crown segment. A multivariate kNN model for timber volume, stem density, basal area and quadratic mean diameter with the semi-ITC approach produced RMSEs of 30%, 46%, 25%, 26%, respectively. The corresponding measures for the ABA were 30%, 51%, 26%, 35%, respectively. Univariate kNN models resulted in timber volume RMSEs of 25% for the semi-ITC approach and 22% for the ABA. A non-linear logistic regression model with the ABA produced an RMSE of 23%. Both approaches predicted timber volume with comparable precision and accuracy at the plot level. The multivariate kNN model was slightly more precise with the semi-ITC approach, while biases were larger.

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Section: Discussionmentioning

confidence: 97%

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Rahlf

Breidenbach

Solberg

et al. 2015

Forests

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“…The methods based on error propagation theory were used to quantify the uncertainty of individual tree biomass following Berger, Gschwantner, McRoberts, & Schadauer (2014) who provided detailed descriptions of the methodology. Briefly, the methodology quantified the uncertainty in terms of the relative standard deviation (SD), and was composed of four parts (Fig.…”

Section: Overview Of Methodsmentioning

confidence: 99%

“…Berger, Gschwantner, McRoberts, & Schadauer (2014) showed that a relationship exists between the SD and the size of tree. Therefore, the relationship between the SD and size for each measurement parameter (i.e.…”

Section: The Sd Of Measurementsmentioning

confidence: 99%

“…Elzinga, C., Shearer, & Elzinga, G., 2005;Kitahara, Mizoue, & Yoshida, 2008). However, the studies focusing on the effect of these errors on the estimation of individual tree biomass are considerably few (Berger, Gschwantner, McRoberts, & Schadauer, 2014). Berger, Gschwantner, McRoberts, & Schadauer (2014) assessed the effects of errors in measurements on estimating individual tree volume using Austrian national forest inventory data.…”

Section: Introductionmentioning

confidence: 99%

“…However, the studies focusing on the effect of these errors on the estimation of individual tree biomass are considerably few (Berger, Gschwantner, McRoberts, & Schadauer, 2014). Berger, Gschwantner, McRoberts, & Schadauer (2014) assessed the effects of errors in measurements on estimating individual tree volume using Austrian national forest inventory data. Mason et al (2014) also assessed the effects of measurement errors on carbon storage estimates in shrubland.…”

Section: Introductionmentioning

confidence: 99%

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The Importance of Tree Height in Estimating Individual Tree Biomass while Considering Errors in Measurements and Allometric Models

et al. 2018

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This study evaluated the uncertainty of individual tree biomass estimated by allometric models by both including and excluding tree height independently. Using two independent sets of measurements on the same trees, the errors in the measurement of diameter at breast height and tree height were quantified, and the uncertainty of individual tree biomass estimation caused by errors in measurement was calculated. For both allometric models, the uncertainties of the individual tree biomass estimation caused by the use of a specific allometric model were also calculated. Finally, the overall uncertainty of individual tree biomass by combining the two uncertainties was calculated. The allometric model including tree height was 6 % more accurate than that excluding tree height when the uncertainty caused by allometric models became the only consideration. However, in terms of the uncertainty caused by measurement, the allometric model excluding tree height was three times more accurate than allometric model including tree height. As a result, the allometric model excluding tree height was 5 % more accurate than the allometric model including tree height when both causes of uncertainty, the allometric model and measurement errors were considered. In conclusion, errors in tree height measurement have the potential to increase the error of aboveground biomass estimation.

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Conducting an Inventory

West¹

2015

Tree and Forest Measurement

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Effects of Measurement Errors on Individual Tree Stem Volume Estimates for the Austrian National Forest Inventory

Cited by 66 publications

References 29 publications

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

The Importance of Tree Height in Estimating Individual Tree Biomass while Considering Errors in Measurements and Allometric Models

Conducting an Inventory

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